Overview

In this lab, you use Machine Learning APIs from within AI Platform Notebooks.

What you learn

In this lab, you learn how to invoke ML APIs from AI Platform Notebooks and use their results.

Introduction

In this lab, you will first

• Clone the code repo within your AI Platform Notebooks environment

and then invoke ML APIs from AI Platform Notebooks to carry out some representative tasks:

• Vision API to detect text in an image
• Translate API to translate that text into English
• Natural Language API to find the sentiment of some famous quotes
• Speech API to transcribe an audio file

ML APIs are microservices. When we build AI models ourselves, it should be our goal to make them so easy to use and stand-alone.

Enable APIs

• Ensure the Cloud Source Repositories API is enabled: https://console.cloud.google.com/apis/library/sourcerepo.googleapis.com/?q=Repositories

Setup

For each lab, you get a new Google Cloud project and set of resources for a fixed time at no cost.

1. Make sure you signed into Qwiklabs using an incognito window.

2. Note the lab's access time (for example, and make sure you can finish in that time block.

3. When ready, click .

4. Note your lab credentials. You will use them to sign in to the Google Cloud Console.

5. Click Open Google Console.

6. Click Use another account and copy/paste credentials for this lab into the prompts.

7. Accept the terms and skip the recovery resource page.

Launch AI Platform Notebooks

To launch AI Platform Notebooks:

Step 1

Click on the Navigation Menu. Navigate to AI Platform, then to Notebooks.

Step 2

On the Notebook instances page, click . Select the latest version of TensorFlow Enterprise 2.x Without GPUs.

In the pop-up, confirm the name of the deep learning VM, move to the bottom of the window and click Create.

The new VM will take 2-3 minutes to start.

Step 3

Click Open JupyterLab. A JupyterLab window will open in a new tab.

Clone course repo within your AI Platform Notebooks instance

To clone the training-data-analyst notebook in your JupyterLab instance:

Step 1

In JupyterLab, click the Terminal icon to open a new terminal.

Step 2

At the command-line prompt, type in the following command and press Enter.

git clone https://github.com/GoogleCloudPlatform/training-data-analystcontent_copy

Step 3

Confirm that you have cloned the repository by double clicking on the training-data-analyst directory and ensuring that you can see its contents. The files for all the Jupyter notebook-based labs throughout this course are available in this directory.

Enable APIs and Get API key

To get an API key:

Step 1

From the GCP console menu, select APIs and services and select Library.

Step 2

In the search box, type vision to find the Google Cloud Vision API and click on the hyperlink.

Step 3

Click Enable if necessary.

Step 4

Follow the same process to enable Translate API, Speech-to-Text API, and Natural Language APIs.

Step 5

From the GCP console menu, select APIs and services and select Credentials.

Step 6

If you do not already have an API key, click the Create credentials button and select API key. Once created, click close. You will need this API key in the notebook later.

Invoke ML APIs from AI Platform Notebooks

Step 1

In the notebook interface, navigate to training-data-analyst > CPB100 > lab4c > mlapis.ipynb.

Step 2

Read the commentary, then run the Python snippets (Use Shift+Enter to run each piece of code) in the cell, step by step. Make sure to insert your API Key in the first Python cell.

End your lab

When you have completed your lab, click End Lab. Qwiklabs removes the resources you’ve used and cleans the account for you.

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